Comparison between the types for the ages and genders

Questions

  • What are the differences between the type for the different combinations of ages and genders?
  • Do we observe the same changes as globally?

Loads

Libraries and functions

Warning message in is.na(x[[i]]):
“is.na() applied to non-(list or vector) of type 'environment'”Warning message in rsqlite_fetch(res@ptr, n = n):
“Don't need to call dbFetch() for statements, only for queries”
==========================================================================
*
*  Package WGCNA 1.63 loaded.
*
*    Important note: It appears that your system supports multi-threading,
*    but it is not enabled within WGCNA in R. 
*    To allow multi-threading within WGCNA with all available cores, use 
*
*          allowWGCNAThreads()
*
*    within R. Use disableWGCNAThreads() to disable threading if necessary.
*    Alternatively, set the following environment variable on your system:
*
*          ALLOW_WGCNA_THREADS=<number_of_processors>
*
*    for example 
*
*          ALLOW_WGCNA_THREADS=4
*
*    To set the environment variable in linux bash shell, type 
*
*           export ALLOW_WGCNA_THREADS=4
*
*     before running R. Other operating systems or shells will
*     have a similar command to achieve the same aim.
*
==========================================================================


Allowing multi-threading with up to 4 threads.
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."

Data

  1. 14447
  2. 88
  1. 0.3609065973726
  2. 0
  1. 4.84577258072278
  2. 0.21654395842356
typeagegender
GF_104w_F_1_2GF 104wF
GF_104w_F_2_2GF 104wF
GF_104w_F_3_2GF 104wF
GF_104w_M_1_2GF 104wM
GF_104w_M_2_2GF 104wM
GF_52w_M_1_2GF 52w M
GF_52w_M_2_2GF 52w M
GF_52w_M_3_2GF 52w M
GF_52w_M_4_2GF 52w M
GF_8w_M_1_2GF 8w M
GF_8w_M_2_2GF 8w M
GF_8w_M_3_2GF 8w M
GF_8w_M_4_2GF 8w M
SPF_104w_F_1_2SPF 104wF
SPF_104w_F_2_2SPF 104wF
SPF_104w_F_3_2SPF 104wF
SPF_104w_M_1_2SPF 104wM
SPF_104w_M_2_2SPF 104wM
SPF_104w_M_3_2SPF 104wM
SPF_104w_M_4_2SPF 104wM
SPF_52w_M_1_2SPF 52w M
SPF_52w_M_2_2SPF 52w M
SPF_52w_M_3_2SPF 52w M
SPF_52w_M_4_2SPF 52w M
SPF_52w_M_5_2SPF 52w M
SPF_8w_M_1_2SPF 8w M
SPF_8w_M_2_2SPF 8w M
SPF_8w_M_3_2SPF 8w M
SPF_8w_M_4_2SPF 8w M
GF_52w_F_1_2GF 52w F
⋮⋮⋮⋮
GF_52w_F_5_2GF 52w F
GF_52w_F_6_2GF 52w F
SPF_52w_F_1_2SPF 52w F
SPF_52w_F_2_2SPF 52w F
SPF_52w_F_3_2SPF 52w F
SPF_52w_F_4_2SPF 52w F
SPF_52w_F_5_2SPF 52w F
SPF_52w_F_6_2SPF 52w F
SPF_104w_M_5_2SPF 104wM
SPF_104w_M_6_2SPF 104wM
SPF_104w_M_7_2SPF 104wM
SPF_104w_M_8_2SPF 104wM
SPF_104w_M_9_2SPF 104wM
SPF_104w_M_10_2SPF 104wM
SPF_104w_M_11_2SPF 104wM
SPF_104w_M_12_2SPF 104wM
SPF_104w_M_13_2SPF 104wM
SPF_104w_M_14_2SPF 104wM
SPF_8w_F_1_2SPF 8w F
SPF_8w_F_3_2SPF 8w F
SPF_8w_F_4_2SPF 8w F
SPF_8w_F_5_2SPF 8w F
GF_8w_F_1_2GF 8w F
GF_8w_F_2_2GF 8w F
GF_8w_F_3_2GF 8w F
GF_8w_F_4_2GF 8w F
GF_8w_F_5_2GF 8w F
GF_104w_M_3_2GF 104wM
GF_104w_M_5_2GF 104wM
GF_104w_M_4_2GF 104wM
typegenderage
SPF_8w_M_1_2SPFM 8w
SPF_8w_M_2_2SPFM 8w
SPF_8w_M_3_2SPFM 8w
SPF_8w_M_4_2SPFM 8w
GF_8w_M_1_2GF M 8w
GF_8w_M_2_2GF M 8w
GF_8w_M_3_2GF M 8w
GF_8w_M_4_2GF M 8w
SPF_8w_F_1_2SPFF 8w
SPF_8w_F_3_2SPFF 8w
SPF_8w_F_4_2SPFF 8w
SPF_8w_F_5_2SPFF 8w
GF_8w_F_1_2GF F 8w
GF_8w_F_2_2GF F 8w
GF_8w_F_3_2GF F 8w
GF_8w_F_4_2GF F 8w
GF_8w_F_5_2GF F 8w
SPF_52w_M_1_2SPFM 52w
SPF_52w_M_2_2SPFM 52w
SPF_52w_M_3_2SPFM 52w
SPF_52w_M_4_2SPFM 52w
SPF_52w_M_5_2SPFM 52w
GF_52w_M_1_2GF M 52w
GF_52w_M_2_2GF M 52w
GF_52w_M_3_2GF M 52w
GF_52w_M_4_2GF M 52w
SPF_52w_F_1_2SPFF 52w
SPF_52w_F_2_2SPFF 52w
SPF_52w_F_3_2SPFF 52w
SPF_52w_F_4_2SPFF 52w
⋮⋮⋮⋮
GF_52w_F_2_2GF F 52w
GF_52w_F_3_2GF F 52w
GF_52w_F_4_2GF F 52w
GF_52w_F_5_2GF F 52w
GF_52w_F_6_2GF F 52w
SPF_104w_M_1_2SPF M 104w
SPF_104w_M_2_2SPF M 104w
SPF_104w_M_3_2SPF M 104w
SPF_104w_M_4_2SPF M 104w
SPF_104w_M_5_2SPF M 104w
SPF_104w_M_6_2SPF M 104w
SPF_104w_M_7_2SPF M 104w
SPF_104w_M_8_2SPF M 104w
SPF_104w_M_9_2SPF M 104w
SPF_104w_M_10_2SPF M 104w
SPF_104w_M_11_2SPF M 104w
SPF_104w_M_12_2SPF M 104w
SPF_104w_M_13_2SPF M 104w
SPF_104w_M_14_2SPF M 104w
GF_104w_M_1_2GF M 104w
GF_104w_M_2_2GF M 104w
GF_104w_M_3_2GF M 104w
GF_104w_M_5_2GF M 104w
GF_104w_M_4_2GF M 104w
SPF_104w_F_1_2SPF F 104w
SPF_104w_F_2_2SPF F 104w
SPF_104w_F_3_2SPF F 104w
GF_104w_F_1_2GF F 104w
GF_104w_F_2_2GF F 104w
GF_104w_F_3_2GF F 104w

Differentially expressed genes

Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in stack.default(getgo(rownames(l$sign_fc_deg), "mm10", "geneSymbol")):
“non-vector elements will be ignored”Warning message in stack.default(getgo(rownames(as.data.frame(l$deg)), "mm10", "geneSymbol", :
“non-vector elements will be ignored”
Warning message:
“Removed 17652 rows containing non-finite values (stat_density).”

Stats

All DEG (Wald padj < 0.05)All over-expressed genes (Wald padj < 0.05 & FC > 0)All under-expressed genes (Wald padj < 0.05 & FC < 0)DEG (Wald padj < 0.05 & abs(FC) >= 1.5)Over-expressed genes (Wald padj < 0.05 & FC >= 1.5)Under-expressed genes (Wald padj < 0.05 & FC <= -1.5)
GF VS SPF (F, 8w) 349 175 174 195103 92
GF VS SPF (M, 8w) 166 90 76 104 58 46
GF VS SPF (F, 52w) 737 315 422 320123 197
GF VS SPF (M, 52w) 403 160 243 240 85 155
GF VS SPF (F, 104w)1767 777 990 992291 701
GF VS SPF (M, 104w)3100140416961219326 893

All DEG (Wald padj < 0.05)

DEG (Wald padj < 0.05 & abs(FC) > 1.5)

DEG (Wald padj < 0.05 & abs(FC) > 1.5)

Log2FC

compgenderage
GF VS SPF (F, 8w)GF VS SPFF 8w
GF VS SPF (M, 8w)GF VS SPFM 8w
GF VS SPF (F, 52w)GF VS SPFF 52w
GF VS SPF (M, 52w)GF VS SPFM 52w
GF VS SPF (F, 104w)GF VS SPFF 104w
GF VS SPF (M, 104w)GF VS SPFM 104w

Z-score

Column order: gender - age - type

Column order: age - gender - type

Co-expression (WGCNA)

DEG into gene co-expression network

  • White: up-regulated
  • Black: down-regulated
GF vs SPF 8w 52w 104w
F
M

Z-score in modules

Column order: gender - age - type

Column order: age - gender - type

Genes in modules

GO analysis

Biological process

Dot-plot with the 20 most significant p-values for the different comparison

Using term, id as id variables
Using term, id as id variables

Network based on description similarity

GF vs SPF 8w 52w 104w
F
M

GF VS SPF (F, 8w)

GO Tree at "../results/dge/type-effect/type_gender_age/go/GF_VS_SPF_F_8w.png"

GF VS SPF (M, 8w)

GO Tree at "../results/dge/type-effect/type_gender_age/go/GF_VS_SPF_M_8w.png"

GF VS SPF (F, 52w)

GO Tree at "../results/dge/type-effect/type_gender_age/go/GF_VS_SPF_F_52w.png"

GF VS SPF (M, 52w)

GO Tree at "../results/dge/type-effect/type_gender_age/go/GF_VS_SPF_M_52w.png"

GF VS SPF (F, 104w)

GO Tree at "../results/dge/type-effect/type_gender_age/go/GF_VS_SPF_F_104w.png"

GF VS SPF (M, 104w)

GO Tree at "../results/dge/type-effect/type_gender_age/go/GF_VS_SPF_M_104w.png"

Cellular components

Dot-plot with the most over-represented CC GO (20 most significant p-values for the different comparison)

Using term, id as id variables

Molecular functions

Dot-plot with the most over-represented MF GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

KEGG pathways

Error in `$<-.data.frame`(`*tmp*`, labels, value = c("", "", "", "", "", : replacement has 33 rows, data has 37
Traceback:

1. plot_kegg_pathways(type_gender_age_deg$over_represented_KEGG[, 
 .     "category"], type_gender_age_deg$fc_deg, "../results/dge/type-effect/type_gender_age/kegg/over_repr_kegg/")
2. suppressMessages(pathview(gene.data = fc_deg, pathway.id = cat, 
 .     species = "Mus musculus", gene.idtype = "Symbol"))
3. withCallingHandlers(expr, message = function(c) invokeRestart("muffleMessage"))
4. pathview(gene.data = fc_deg, pathway.id = cat, species = "Mus musculus", 
 .     gene.idtype = "Symbol")
5. `$<-`(`*tmp*`, labels, value = c("", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", ""))
6. `$<-.data.frame`(`*tmp*`, labels, value = c("", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", ""))
7. stop(sprintf(ngettext(N, "replacement has %d row, data has %d", 
 .     "replacement has %d rows, data has %d"), N, nrows), domain = NA)

Pathway graphs available at ../results/dge/type-effect/type_gender_age/over_repr_kegg/

Pathway graphs available at ../results/dge/type-effect/type_gender_age/under_repr_kegg/

Comparison with Erny results

Protocol: 2 months old female mices (GF vs SPF)

Raw comparison of the results

Detailed comparison

  • Checking the correlation between the counts of Erny and our counts
  • Re-running a DGE analysis on the Erny's raw counts